[1]靳一,王继武,吴乐南.混合蛙跳算法优化的支持向量机EBPSK检测器[J].东南大学学报(自然科学版),2011,41(3):509-512.[doi:10.3969/j.issn.1001-0505.2011.03.015]
　Jin Yi,Wang Jiwu,Wu Lenan.EBPSK demodulator based on shuffled frog leaping optimized SVM[J].Journal of Southeast University (Natural Science Edition),2011,41(3):509-512.[doi:10.3969/j.issn.1001-0505.2011.03.015]
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混合蛙跳算法优化的支持向量机EBPSK检测器()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
41
期数:
2011年第3期
页码:
509-512
栏目:
信息与通信工程
出版日期:
2011-05-20

文章信息/Info

Title:
EBPSK demodulator based on shuffled frog leaping optimized SVM
作者:
靳一; 王继武; 吴乐南
(东南大学信息科学与工程学院,南京 210096)
Author(s):
Jin Yi; Wang Jiwu; Wu Lenan
(School of Information Science and Engineering, Southeast University, Nanjing 210096, China)
关键词:
冲击滤波器; 支持向量机; 混合蛙跳算法; EBPSK; 幅度积分判决
Keywords:
impacting filter;  support vector machine(SVM);  shuffled frog leaping algorithm;  extended binary phase shift keying(EBPSK);  magnitude integral detector
分类号:
TN911
DOI:
10.3969/j.issn.1001-0505.2011.03.015
摘要:
为了充分利用“0”,“1”码元经过冲击滤波器后的波形差异和有效改善基本支持向量机经典训练方法容易陷入局部最优的缺陷,设计了混合蛙跳算法优化的支持向量机EBPSK检测器.首先,从经过冲击滤波器的“0”,“1”码元原始数据中提取训练集和测试集,并进行归一化处理; 然后,利用混合蛙跳算法的全局寻优能力在训练集空间搜索支持向量机的支持向量和分类阈值,并用训练过的支持向量机对测试集分类.将混合蛙跳算法优化的支持向量机的检测效果与基本支持向量机以及幅度积分判决进行了对比,结果表明:基本支持向量机检测效果要好于幅度积分判决,混合蛙跳算法优化的支持向量机具有更好的检测精度,检测效果优于前两者.
Abstract:
To take full advantage of the waveform difference of “0” and “1” symbols when passing the impacting filter and improve the defect of easily falling into local optimum when using basic support vector machine(SVM), the SVM detector based on the shuffled frog leaping algorithm(SFLA) is designed in the extended binary phase shift keying(EBPSK) communication system. First, the training set and the testing set are extracted from the original “0”, “1”symbols which pass the impacting filter, and are normalized. Then, the SFLA is introduced to search the support vector and the classification threshold of SVM in the training set, and the testing set is classified using the trained SVM. The detection result of the SVM optimized by the SFLA is compared with that of SVM detector and magnitude integral detector. The comparison shows that the detection performance of basic SVM is better than that of magnitude integration, and the SVM optimized by the SFLA has the best performance.

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相似文献/References:

[1]常虹,丁佳佳,吴乐南.AWGN信道EBPSK系统解调性能分析[J].东南大学学报(自然科学版),2012,42(1):14.[doi:10.3969/j.issn.1001-0505.2012.01.003]
　Chang Hong,Ding Jiajia,Wu Lenan.Performance of EBPSK demodulator in AWGN channel[J].Journal of Southeast University (Natural Science Edition),2012,42(3):14.[doi:10.3969/j.issn.1001-0505.2012.01.003]

备注/Memo

备注/Memo:
作者简介:靳一(1984—),男,博士生;吴乐南(联系人),男,博士,教授,博士生导师,wuln@seu.edu.cn.
基金项目:国家自然科学基金资助项目(60872075)、国家高技术研究发展计划(863计划)资助项目(2008AA01Z227).
引文格式: 靳一,王继武,吴乐南.混合蛙跳算法优化的支持向量机EBPSK检测器[J].东南大学学报:自然科学版,2011,41(3):509-512.[doi:10.3969/j.issn.1001-0505.2011.03.015]
更新日期/Last Update: 2011-05-20